{"id":"W2524143095","doi":"10.3233/jcm-160653","title":"Iterated similarity sequences and factorial level similarities in databases","year":2016,"lang":"en","type":"article","venue":"Journal of Computational Methods in Sciences and Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Iterated function; Similarity (geometry); Constraint (computer-aided design); Simple (philosophy); Mathematics; Database; Computer science; Property (philosophy); Similitude; Data mining; Algorithm; Theoretical computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002114208,0.00007771586,0.0001535603,0.0003540047,0.00004258335,0.0001742427,0.0002894103,0.00001659795,0.000002043818],"category_scores_gemma":[0.0003023544,0.00005152843,0.0000149071,0.0003745345,0.00008359127,0.001834228,0.0001477531,0.00008239233,7.257321e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001918193,"about_ca_system_score_gemma":0.00004479321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001143357,"about_ca_topic_score_gemma":0.000003830835,"domain_scores_codex":[0.9991006,0.00007657037,0.0002994386,0.0001525168,0.0002344447,0.0001364924],"domain_scores_gemma":[0.998861,0.0009100549,0.00009021232,0.00004736807,0.00004609224,0.00004529255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002894881,0.00009992406,0.04164906,0.0001282701,0.00004431139,0.0001537621,0.001641701,0.200432,0.005113135,0.1735444,0.0001702519,0.5769942],"study_design_scores_gemma":[0.0009547964,0.0001556456,0.1313026,0.0003966035,0.00000463515,0.00008642375,0.0001138359,0.8043413,0.0004848933,0.06053177,0.001356241,0.0002712512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05800408,0.0002684744,0.9405537,0.0006670968,0.0004438095,0.00002773744,0.000006065923,0.000006784882,0.00002222392],"genre_scores_gemma":[0.2120466,0.00009797789,0.7877569,0.00003647073,0.00005665731,5.493622e-7,2.568419e-7,0.000001483385,0.000003173283],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6039093,"threshold_uncertainty_score":0.2101268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.127765687047291,"score_gpt":0.3856599203283524,"score_spread":0.2578942332810614,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}